Functions and functional programming

class: center, middle, title-slide

# Functions and functional programming
### Claus O. Wilke
### last updated: 2021-09-23

---

## We often have to run similar code multiple times

.tiny-font.pull-left.width-50[

```r
penguins %>%
  filter(species == "Adelie") %>%
  ggplot() +
  aes(bill_length_mm, body_mass_g) +
  geom_point() +
  ggtitle("Species: Adelie") +
  xlab("bill length (mm)") +
  ylab("body mass (g)") +
  theme_minimal_grid() +
  theme(plot.title.position = "plot")
```
]

.pull-right.width-45.move-up-1em[
![](functional-programming_files/figure-html/penguins-plot-Adelie-out-1.svg)
]

---

## We often have to run similar code multiple times

.tiny-font.pull-left.width-50[

```r
penguins %>%
  filter(species == "Chinstrap") %>%
  ggplot() +
  aes(bill_length_mm, body_mass_g) +
  geom_point() +
  ggtitle("Species: Chinstrap") +
  xlab("bill length (mm)") +
  ylab("body mass (g)") +
  theme_minimal_grid() +
  theme(plot.title.position = "plot")
```
]

.pull-right.width-45.move-up-1em[
![](functional-programming_files/figure-html/penguins-plot-Chinstrap-out-1.svg)
]

---

## We often have to run similar code multiple times

.tiny-font.pull-left.width-50[

```r
penguins %>%
  filter(species == "Gentoo") %>%
  ggplot() +
  aes(bill_length_mm, body_mass_g) +
  geom_point() +
  ggtitle("Species: Gentoo") +
  xlab("bill length (mm)") +
  ylab("body mass (g)") +
  theme_minimal_grid() +
  theme(plot.title.position = "plot")
```
]

.pull-right.width-45.move-up-1em[
![](functional-programming_files/figure-html/penguins-plot-Gentoo-out-1.svg)
]

How can we make our life simpler and avoid massive code duplication?

---

## Step 1: Avoid hard-coding specific values

.tiny-font.pull-left.width-50[

```r
*species <- "Adelie"

penguins %>%
* filter(.data$species == .env$species) %>%
  ggplot() +
  aes(bill_length_mm, body_mass_g) +
  geom_point() +
* ggtitle(glue("Species: {species}")) +
  xlab("bill length (mm)") +
  ylab("body mass (g)") +
  theme_minimal_grid() +
  theme(plot.title.position = "plot")
```
]

.pull-right.width-45.move-up-1em[
![](functional-programming_files/figure-html/penguins-plot-Adelie2-out-1.svg)
]

---

## A quick aside: the pronouns `.data` and `.env`

We can use pronouns to distinguish data columns from variables:
```r
species <- "Adelie"

penguins %>%
  filter(.data$species == .env$species)
```

`.data$species` is a column in the data frame

`.env$species` is a variable in the local environment

---

## A quick aside: the pronouns `.data` and `.env`

Alternatively we would have to make sure the names don't clash:

```r
species_choice <- "Adelie"

penguins %>%
  filter(species == species_choice)
```

---

## Step 1: Avoid hard-coding specific values

.tiny-font.pull-left.width-50[

```r
*species <- "Adelie"

penguins %>%
  filter(.data$species == .env$species) %>%
  ggplot() +
  aes(bill_length_mm, body_mass_g) +
  geom_point() +
  ggtitle(glue("Species: {species}")) +
  xlab("bill length (mm)") +
  ylab("body mass (g)") +
  theme_minimal_grid() +
  theme(plot.title.position = "plot")
```
]

.pull-right.width-45.move-up-1em[
![](functional-programming_files/figure-html/penguins-plot-Adelie2-out-1.svg)
]

---

## Step 1: Avoid hard-coding specific values

.tiny-font.pull-left.width-50[

```r
*species <- "Chinstrap"

.pull-right.width-45.move-up-1em[
![](functional-programming_files/figure-html/penguins-plot-Chinstrap2-out-1.svg)
]

---

## Step 1: Avoid hard-coding specific values

.tiny-font.pull-left.width-50[

```r
*species <- "Gentoo"

.pull-right.width-45.move-up-1em[
![](functional-programming_files/figure-html/penguins-plot-Gentoo2-out-1.svg)
]

This concept is also called: .highlight[Avoiding magic numbers]

---

## Step 2: Define a function

.tiny-font.pull-left.width-50[

```r
*make_plot <- function(species) {
  penguins %>%
    filter(.data$species == .env$species) %>%
    ggplot() +
    aes(bill_length_mm, body_mass_g) +
    geom_point() +
    ggtitle(glue("Species: {species}")) +
    xlab("bill length (mm)") +
    ylab("body mass (g)") +
    theme_minimal_grid() +
    theme(plot.title.position = "plot")
}
```
]
---

## Step 2: Define a function

.tiny-font.pull-left.width-50[

```r
make_plot <- function(species) {
  penguins %>%
    filter(.data$species == .env$species) %>%
    ggplot() +
    aes(bill_length_mm, body_mass_g) +
    geom_point() +
    ggtitle(glue("Species: {species}")) +
    xlab("bill length (mm)") +
    ylab("body mass (g)") +
    theme_minimal_grid() +
    theme(plot.title.position = "plot")
}

*make_plot("Adelie")
```
]

.pull-right.width-45.move-up-1em[
![](functional-programming_files/figure-html/penguins-plot-Adelie3-out-1.svg)
]

---

## Step 2: Define a function

.small-font[

```r
make_plot("Adelie")
make_plot("Chinstrap")
make_plot("Gentoo")
```
]

---

## Rules of thumb about functions

- You can never write too many functions

- When you find yourself writing the same code 2-3 times, put it into a function

- A function should be no longer than 20-40 lines

- If a function is getting too long, break it into smaller functions

---

## Step 3: Automate calling the function

.small-font[

```r
species <- c("Adelie", "Chinstrap", "Gentoo")
plots <- map(species, make_plot)
```
]

`map` takes each element of the vector `species` and uses it as input for `make_plot()`

It returns a list of created plots:

.small-font[

```r
plots[[1]]
```

<img src="functional-programming_files/figure-html/penguins-plot-map-return-1.svg" width="32%" />
]

---

## Step 3: Automate calling the function

.small-font[

```r
species <- c("Adelie", "Chinstrap", "Gentoo")
plots <- map(species, make_plot)
```
]

`map` takes each element of the vector `species` and uses it as input for `make_plot()`

It returns a list of created plots:

.small-font[

```r
plots[[2]]
```

<img src="functional-programming_files/figure-html/penguins-plot-map-return2-1.svg" width="32%" />
]

---

## Step 3: Automate calling the function

.small-font[

```r
species <- c("Adelie", "Chinstrap", "Gentoo")
plots <- map(species, make_plot)
```
]

`map` takes each element of the vector `species` and uses it as input for `make_plot()`

It returns a list of created plots:

.small-font[

```r
plots[[3]]
```

<img src="functional-programming_files/figure-html/penguins-plot-map-return3-1.svg" width="32%" />
]

---

## Step 3: Automate calling the function

.small-font[

```r
species <- c("Adelie", "Chinstrap", "Gentoo")
plots <- map(species, make_plot)

plots[[1]]
plots[[2]]
plots[[3]]
```
]

---

## Step 3: Automate calling the function

.small-font[

```r
species <- c("Adelie", "Chinstrap", "Gentoo")
plots <- map(species, make_plot)

# `walk()` is like `map()` but doesn't return a value
# we use it only for side effects (such as printing)
walk(plots, print)
```
]

---

## Step 4: Write a more general function

.tiny-font.pull-left.width-50[

```r
make_plot <- function(species) {
* penguins %>% # hard-coded dataset!
    filter(.data$species == .env$species) %>%
    ggplot() +
    aes(bill_length_mm, body_mass_g) +
    geom_point() +
    ggtitle(glue("Species: {species}")) +
    xlab("bill length (mm)") +
    ylab("body mass (g)") +
    theme_minimal_grid() +
    theme(plot.title.position = "plot")
}
```
]

---

## Step 4: Write a more general function

.tiny-font.pull-left.width-50[

```r
*make_plot2 <- function(data, species) {
* data %>%
    # filter no longer needed
    ggplot() +
    aes(bill_length_mm, body_mass_g) +
    geom_point() +
    ggtitle(glue("Species: {species}")) +
    xlab("bill length (mm)") +
    ylab("body mass (g)") +
    theme_minimal_grid() +
    theme(plot.title.position = "plot")
}
```
]

---

## Step 4: Write a more general function

.tiny-font.pull-left.width-50[

```r
make_plot2 <- function(data, species) {
  data %>%
    # filter no longer needed
    ggplot() +
    aes(bill_length_mm, body_mass_g) +
    geom_point() +
    ggtitle(glue("Species: {species}")) +
    xlab("bill length (mm)") +
    ylab("body mass (g)") +
    theme_minimal_grid() +
    theme(plot.title.position = "plot")
}

data_adelie <- penguins %>%
  filter(species == "Adelie")

make_plot2(data_adelie, species = "Adelie")
```
]

.pull-right.width-45[
![](functional-programming_files/figure-html/penguins-plot-generic-out-1.svg)
]

---

## Step 5: Use these concepts in a tidy pipeline

.tiny-font[

```r
penguins %>%
  nest(data = -species) %>%
  mutate(plots = map(species, make_plot))
```

```
# A tibble: 3 × 3
  species   data               plots 
  <fct>     <list>             <list>
1 Adelie    <tibble [152 × 7]> <gg>  
2 Gentoo    <tibble [124 × 7]> <gg>  
3 Chinstrap <tibble [68 × 7]>  <gg>  
```
]

---

## Step 5: Use these concepts in a tidy pipeline

.tiny-font[

```r
penguins %>%
  nest(data = -species) %>%
  mutate(plots = map(species, make_plot)) %>%
  pull(plots) %>%
  walk(print)
```
]

---

## Step 5: Use these concepts in a tidy pipeline

.tiny-font[

```r
penguins %>%
  nest(data = -species) %>%
  mutate(plots = map2(data, species, make_plot2)) %>%
  pull(plots) %>%
  walk(print)
```
]

.small-font[
`map2()` is like `map()` but for functions with 2 arguments
]

---

## Step 5: Use these concepts in a tidy pipeline

.tiny-font[

```r
penguins %>%
  nest(data = -species) %>%
  mutate(plots = map2(data, species, make_plot2)) %>%
  pull(plots) %>%
  walk(print)
```
]

.small-font[
Note: This pipeline automatically processes all species in the dataset, whatever they are called
]

---

## Why no `for` loops?

- They often require us to think about data logistics (indexing)

- They encourage writing long, monolithic blocks of code

- They encourage iterative thinking over conceptual thinking

- They cannot easily be parallelized or otherwise optimized

- Most modern programming languages are moving away from `for` loops<br>
  (examples: Python, Rust, JavaScript)

[//]: # "segment ends here"

---

## Further reading

- R for Data Science: [Chapter 19: Functions](https://r4ds.had.co.nz/functions.html)
- R for Data Science: [Chapter 21.5: The map functions](https://r4ds.had.co.nz/iteration.html#the-map-functions)
- **purrr** reference documentation: [Apply a function to each element of a list or atomic vector](https://purrr.tidyverse.org/reference/map.html)